Great question! Rudin et al.’s approach elegantly builds an optimal decision tree through search. TM learns online, processing one example at a time, like a neural network. Also, like logistic regression, TM adds up evidence from different features, however, it builds non-linear logical rules, instead of operating on single features. TM also supports convolution for image processing and time series. It can also learn from penalties and rewards addressing the contextual bandit problem. Finally, TMs allow self-supervised learning by means of an auto-encoder. So, quite different from decision trees.